| --- |
| license: mit |
| tags: |
| - video-classification |
| - event-detection |
| --- |
| # Team ERIS: IWDD 2027 illegal waste dumping detection |
|
|
| Weights of Team ERIS's entry to the International Contest on Illegal Waste Dumping Detection (IWDD 2027). |
| A frozen ImageNet-21k ViT-B/16 with bottleneck adapters and a temporal transformer head classifies 3.2 s windows |
| (16 frames at 5 fps) as normal or dumping; a threshold decoder turns the window probabilities into one onset per video. |
|
|
| `iwdd_s4.pth` holds `{'model': state_dict}` for the `VideoModel` in the submission's `model.py`; |
| `test.py` downloads it from this repository when no local copy is given. |
|
|
| ## Paper and code |
|
|
| This model is the Window Classifier of the paper "Parameter-Efficient Image-to-Video Adaptation for Action Spotting of |
| Illegal Waste Dumping" (WACV 2027 Workshops, WasteVision). The code is at |
| https://github.com/arkel23/PEI2V_WasteVision27; its README section "Pretrained weights" shows how to download this file |
| and score the public-test windows with `tools.train --test_only`. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{rios2027pei2v, |
| title={Parameter-Efficient Image-to-Video Adaptation for Action Spotting of Illegal Waste Dumping}, |
| author={Rios, Edwin Arkel and Hu, Min-Chun and Lai, Bo-Cheng}, |
| booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)}, |
| year={2027} |
| } |
| |
| @inproceedings{greco2027evolution, |
| title={The Evolution of Illegal Waste Dumping Detection in the Age of Foundation Models}, |
| author={Greco, Antonio and Ricciardi, Andrea Vincenzo and Sansone, Carlo and Vento, Bruno}, |
| booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)}, |
| year={2027} |
| } |
| ``` |
|
|
